'''simple docstring'''






import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

   if is_vision_available():
      from transformers import MaskFormerImageProcessor

if is_vision_available():
   from PIL import Image







class lowercase_       :







   """simple docstring"""

   def __init__(      self      :			Optional[Any], UpperCamelCase__      :			Tuple, UpperCamelCase__      :			Any=2, UpperCamelCase__      :			Union[str, Any]=True, UpperCamelCase__      :			Optional[Any]=False, UpperCamelCase__      :			int=10, UpperCamelCase__      :			List[str]=3, UpperCamelCase__      :			List[str]=32 * 4, UpperCamelCase__      :			List[Any]=32 * 6, UpperCamelCase__      :			Dict=4, UpperCamelCase__      :			str=32, )  ->			Dict:
        _A    		=  parent
        _A    		=  batch_size
        _A    		=  is_training
        _A    		=  use_auxiliary_loss
        _A    		=  num_queries
        _A    		=  num_channels
        _A    		=  min_size
        _A    		=  max_size
        _A    		=  num_labels
        _A    		=  mask_feature_size
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Tuple:
        _A    		=  floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]     ).to(
            UpperCamelCase__     )

        _A    		=  torch.ones([self.batch_size, self.min_size, self.max_size], device=UpperCamelCase__     )

        _A    		=  (
            torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size], device=UpperCamelCase__     ) > 0.5
        ).float()
        _A    		=  (torch.rand((self.batch_size, self.num_labels), device=UpperCamelCase__     ) > 0.5).long()

        _A    		=  self.get_config()
        return config, pixel_values, pixel_mask, mask_labels, class_labels
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Optional[int]:
        return MaskFormerConfig.from_backbone_and_decoder_configs(
            backbone_config=SwinConfig(
                depths=[1, 1, 1, 1], ), decoder_config=DetrConfig(
                decoder_ffn_dim=1_28, num_queries=self.num_queries, decoder_attention_heads=2, d_model=self.mask_feature_size, ), mask_feature_size=self.mask_feature_size, fpn_feature_size=self.mask_feature_size, num_channels=self.num_channels, num_labels=self.num_labels, )
   def 	__UpperCAmelCase   (      self      :			int     )  ->			Tuple:
        _A		,       _A		,       _A		,       _A		,       _A    		=  self.prepare_config_and_inputs()
        _A    		=  {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
        return config, inputs_dict
   def 	__UpperCAmelCase   (      self      :			Optional[int], UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			Dict     )  ->			List[Any]:
        _A    		=  output.encoder_hidden_states
        _A    		=  output.pixel_decoder_hidden_states
        _A    		=  output.transformer_decoder_hidden_states

        self.parent.assertTrue(len(UpperCamelCase__     ), len(config.backbone_config.depths     )     )
        self.parent.assertTrue(len(UpperCamelCase__     ), len(config.backbone_config.depths     )     )
        self.parent.assertTrue(len(UpperCamelCase__     ), config.decoder_config.decoder_layers     )
   def 	__UpperCAmelCase   (      self      :			Any, UpperCamelCase__      :			Any, UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			Optional[Any]=False     )  ->			Optional[Any]:
        with torch.no_grad():
             _A    		=  MaskFormerModel(config=UpperCamelCase__     )
             model.to(UpperCamelCase__     )
             model.eval()

             _A    		=  model(pixel_values=UpperCamelCase__, pixel_mask=UpperCamelCase__     )
             _A    		=  model(UpperCamelCase__, output_hidden_states=UpperCamelCase__     )
        # the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
        # encoder and pixel decoder
        self.parent.assertEqual(
            output.transformer_decoder_last_hidden_state.shape, (self.batch_size, self.num_queries, self.mask_feature_size), )
        # let's ensure the other two hidden state exists
        self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None     )
        self.parent.assertTrue(output.encoder_last_hidden_state is not None     )

        if output_hidden_states:
             self.check_output_hidden_state(UpperCamelCase__, UpperCamelCase__     )

   def 	__UpperCAmelCase   (      self      :			List[Any], UpperCamelCase__      :			Tuple, UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			int, UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			Tuple     )  ->			str:
        _A    		=  MaskFormerForInstanceSegmentation(config=UpperCamelCase__     )
        model.to(UpperCamelCase__     )
        model.eval()

        def comm_check_on_output(UpperCamelCase__      :			Any     ):
             # let's still check that all the required stuff is there
             self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None     )
             self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None     )
             self.parent.assertTrue(result.encoder_last_hidden_state is not None     )
             # okay, now we need to check the logits shape
             # due to the encoder compression, masks have a //4 spatial size
             self.parent.assertEqual(
                 result.masks_queries_logits.shape, (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4), )
             # + 1 for null class
             self.parent.assertEqual(
                 result.class_queries_logits.shape, (self.batch_size, self.num_queries, self.num_labels + 1)     )

        with torch.no_grad():
             _A    		=  model(pixel_values=UpperCamelCase__, pixel_mask=UpperCamelCase__     )
             _A    		=  model(UpperCamelCase__     )

             comm_check_on_output(UpperCamelCase__     )

             _A    		=  model(
                 pixel_values=UpperCamelCase__, pixel_mask=UpperCamelCase__, mask_labels=UpperCamelCase__, class_labels=UpperCamelCase__     )

        comm_check_on_output(UpperCamelCase__     )

        self.parent.assertTrue(result.loss is not None     )
        self.parent.assertEqual(result.loss.shape, torch.Size([1]     )     )







@require_torch
class lowercase_       (		_UpperCamelCase    ,    _UpperCamelCase    ,    unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
   __lowerCAmelCase         =   (
       {"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
       if is_torch_available()
       else {}
   )

   __lowerCAmelCase         =   False
   __lowerCAmelCase         =   False
   __lowerCAmelCase         =   False
   __lowerCAmelCase         =   False
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Tuple:
        _A    		=  MaskFormerModelTester(self     )
        _A    		=  ConfigTester(self, config_class=UpperCamelCase__, has_text_modality=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			int:
        self.config_tester.run_common_tests()
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			int:
        _A		,       _A    		=  self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_maskformer_model(UpperCamelCase__, **UpperCamelCase__, output_hidden_states=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Optional[int]:
        _A    		=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*UpperCamelCase__     )
   @unittest.skip(reason='MaskFormer does not use inputs_embeds'     )
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Any:
        pass
   @unittest.skip(reason='MaskFormer does not have a get_input_embeddings method'     )
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Union[str, Any]:
        pass
   @unittest.skip(reason='MaskFormer is not a generative model'     )
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Union[str, Any]:
        pass
   @unittest.skip(reason='MaskFormer does not use token embeddings'     )
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			Union[str, Any]:
        pass
   @require_torch_multi_gpu
   @unittest.skip(
       reason='MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'     )
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Union[str, Any]:
        pass
   @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'     )
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			Any:
        pass
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			List[str]:
        _A		,       _A    		=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             _A    		=  model_class(UpperCamelCase__     )
             _A    		=  inspect.signature(model.forward     )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             _A    		=  [*signature.parameters.keys()]

             _A    		=  ['pixel_values']
             self.assertListEqual(arg_names[:1], UpperCamelCase__     )
   @slow
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			Optional[int]:
        for model_name in ["facebook/maskformer-swin-small-coco"]:
             _A    		=  MaskFormerModel.from_pretrained(UpperCamelCase__     )
             self.assertIsNotNone(UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			List[Any]:
        _A    		=  (self.model_tester.min_size,) * 2
        _A    		=  {
            'pixel_values': torch.randn((2, 3, *size), device=UpperCamelCase__     ),
            'mask_labels': torch.randn((2, 10, *size), device=UpperCamelCase__     ),
            'class_labels': torch.zeros(2, 10, device=UpperCamelCase__     ).long(),
        }

        _A    		=  MaskFormerForInstanceSegmentation(MaskFormerConfig()     ).to(UpperCamelCase__     )
        _A    		=  model(**UpperCamelCase__     )
        self.assertTrue(outputs.loss is not None     )
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Dict:
        _A		,       _A    		=  self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_maskformer_model(UpperCamelCase__, **UpperCamelCase__, output_hidden_states=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			int     )  ->			Tuple:
        _A		,       _A    		=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             _A    		=  model_class(UpperCamelCase__     ).to(UpperCamelCase__     )
             _A    		=  model(**UpperCamelCase__, output_attentions=UpperCamelCase__     )
             self.assertTrue(outputs.attentions is not None     )
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			List[Any]:
        if not self.model_tester.is_training:
             return
        # only MaskFormerForInstanceSegmentation has the loss
        _A    		=  self.all_model_classes[1]
        _A		,       _A		,       _A		,       _A		,       _A    		=  self.model_tester.prepare_config_and_inputs()

        _A    		=  model_class(UpperCamelCase__     )
        model.to(UpperCamelCase__     )
        model.train()

        _A    		=  model(UpperCamelCase__, mask_labels=UpperCamelCase__, class_labels=UpperCamelCase__     ).loss
        loss.backward()

   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			List[str]:
        # only MaskFormerForInstanceSegmentation has the loss
        _A    		=  self.all_model_classes[1]
        _A		,       _A		,       _A		,       _A		,       _A    		=  self.model_tester.prepare_config_and_inputs()
        _A    		=  True
        _A    		=  True

        _A    		=  model_class(UpperCamelCase__     )
        model.to(UpperCamelCase__     )
        model.train()

        _A    		=  model(UpperCamelCase__, mask_labels=UpperCamelCase__, class_labels=UpperCamelCase__     )

        _A    		=  outputs.encoder_hidden_states[0]
        encoder_hidden_states.retain_grad()

        _A    		=  outputs.pixel_decoder_hidden_states[0]
        pixel_decoder_hidden_states.retain_grad()
        # we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
        _A    		=  outputs.transformer_decoder_hidden_states[0]
        transformer_decoder_hidden_states.retain_grad()

        _A    		=  outputs.attentions[0]
        attentions.retain_grad()

        outputs.loss.backward(retain_graph=UpperCamelCase__     )

        self.assertIsNotNone(encoder_hidden_states.grad     )
        self.assertIsNotNone(pixel_decoder_hidden_states.grad     )
        self.assertIsNotNone(transformer_decoder_hidden_states.grad     )
        self.assertIsNotNone(attentions.grad     )






_UpperCAmelCase    :     Any         =       1E-4





def  _SCREAMING_SNAKE_CASE		(      ):
     _A    		=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
     return image







@require_vision
@slow
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   @cached_property
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Optional[int]:
        return (
            MaskFormerImageProcessor.from_pretrained('facebook/maskformer-swin-small-coco'     )
            if is_vision_available()
            else None
        )
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			List[str]:
        _A    		=  MaskFormerModel.from_pretrained('facebook/maskformer-swin-small-coco'     ).to(UpperCamelCase__     )
        _A    		=  self.default_image_processor
        _A    		=  prepare_img()
        _A    		=  image_processor(UpperCamelCase__, return_tensors='pt'     ).to(UpperCamelCase__     )
        _A    		=  inputs['pixel_values'].shape
        # check size is divisible by 32
        self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0     )
        # check size
        self.assertEqual(UpperCamelCase__, (1, 3, 8_00, 10_88)     )

        with torch.no_grad():
             _A    		=  model(**UpperCamelCase__     )

        _A    		=  torch.tensor(
            [[-0.0_482, 0.9_228, 0.4_951], [-0.2_547, 0.8_017, 0.8_527], [-0.0_069, 0.3_385, -0.0_089]]     ).to(UpperCamelCase__     )
        self.assertTrue(
            torch.allclose(
                outputs.encoder_last_hidden_state[0, 0, :3, :3], UpperCamelCase__, atol=UpperCamelCase__     )     )

        _A    		=  torch.tensor(
            [[-0.8_422, -0.8_434, -0.9_718], [-1.0_144, -0.5_565, -0.4_195], [-1.0_038, -0.4_484, -0.1_961]]     ).to(UpperCamelCase__     )
        self.assertTrue(
            torch.allclose(
                outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3], UpperCamelCase__, atol=UpperCamelCase__     )     )

        _A    		=  torch.tensor(
            [[0.2_852, -0.0_159, 0.9_735], [0.6_254, 0.1_858, 0.8_529], [-0.0_680, -0.4_116, 1.8_413]]     ).to(UpperCamelCase__     )
        self.assertTrue(
            torch.allclose(
                outputs.transformer_decoder_last_hidden_state[0, :3, :3], UpperCamelCase__, atol=UpperCamelCase__     )     )
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			Optional[int]:
        _A    		=  (
            MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco'     )
            .to(UpperCamelCase__     )
            .eval()
        )
        _A    		=  self.default_image_processor
        _A    		=  prepare_img()
        _A    		=  image_processor(UpperCamelCase__, return_tensors='pt'     ).to(UpperCamelCase__     )
        _A    		=  inputs['pixel_values'].shape
        # check size is divisible by 32
        self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0     )
        # check size
        self.assertEqual(UpperCamelCase__, (1, 3, 8_00, 10_88)     )

        with torch.no_grad():
             _A    		=  model(**UpperCamelCase__     )
        # masks_queries_logits
        _A    		=  outputs.masks_queries_logits
        self.assertEqual(
            masks_queries_logits.shape, (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4), )
        _A    		=  [
            [-1.3_737_124, -1.7_724_937, -1.9_364_233],
            [-1.5_977_281, -1.9_867_939, -2.1_523_695],
            [-1.5_795_398, -1.9_269_832, -2.093_942],
        ]
        _A    		=  torch.tensor(UpperCamelCase__     ).to(UpperCamelCase__     )
        self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3], UpperCamelCase__, atol=UpperCamelCase__     )     )
        # class_queries_logits
        _A    		=  outputs.class_queries_logits
        self.assertEqual(
            class_queries_logits.shape, (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)     )
        _A    		=  torch.tensor(
            [
                [1.6512e00, -5.2572e00, -3.3519e00],
                [3.6169e-02, -5.9025e00, -2.9313e00],
                [1.0766e-04, -7.7630e00, -5.1263e00],
            ]     ).to(UpperCamelCase__     )
        self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3], UpperCamelCase__, atol=UpperCamelCase__     )     )
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Optional[Any]:
        _A    		=  (
            MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-resnet101-coco-stuff'     )
            .to(UpperCamelCase__     )
            .eval()
        )
        _A    		=  self.default_image_processor
        _A    		=  prepare_img()
        _A    		=  image_processor(UpperCamelCase__, return_tensors='pt'     ).to(UpperCamelCase__     )
        _A    		=  inputs['pixel_values'].shape
        # check size is divisible by 32
        self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0     )
        # check size
        self.assertEqual(UpperCamelCase__, (1, 3, 8_00, 10_88)     )

        with torch.no_grad():
             _A    		=  model(**UpperCamelCase__     )
        # masks_queries_logits
        _A    		=  outputs.masks_queries_logits
        self.assertEqual(
            masks_queries_logits.shape, (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4), )
        _A    		=  [[-0.9_046, -2.6_366, -4.6_062], [-3.4_179, -5.7_890, -8.8_057], [-4.9_179, -7.6_560, -10.7_711]]
        _A    		=  torch.tensor(UpperCamelCase__     ).to(UpperCamelCase__     )
        self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3], UpperCamelCase__, atol=UpperCamelCase__     )     )
        # class_queries_logits
        _A    		=  outputs.class_queries_logits
        self.assertEqual(
            class_queries_logits.shape, (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)     )
        _A    		=  torch.tensor(
            [[4.7_188, -3.2_585, -2.8_857], [6.6_871, -2.9_181, -1.2_487], [7.2_449, -2.2_764, -2.1_874]]     ).to(UpperCamelCase__     )
        self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3], UpperCamelCase__, atol=UpperCamelCase__     )     )

   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			int:
        _A    		=  (
            MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco'     )
            .to(UpperCamelCase__     )
            .eval()
        )
        _A    		=  self.default_image_processor

        _A    		=  image_processor(
            [np.zeros((3, 8_00, 13_33)     ), np.zeros((3, 8_00, 13_33)     )], segmentation_maps=[np.zeros((3_84, 3_84)     ).astype(np.floataa     ), np.zeros((3_84, 3_84)     ).astype(np.floataa     )], return_tensors='pt', )

        _A    		=  inputs['pixel_values'].to(UpperCamelCase__     )
        _A    		=  [el.to(UpperCamelCase__     ) for el in inputs['mask_labels']]
        _A    		=  [el.to(UpperCamelCase__     ) for el in inputs['class_labels']]

        with torch.no_grad():
             _A    		=  model(**UpperCamelCase__     )

        self.assertTrue(outputs.loss is not None     )









'''simple docstring'''







import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_     						=				logging.get_logger(__name__)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase=False						):






   '''simple docstring'''







   A__       =     []
   for i in range(config.num_hidden_layers						):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""deit.encoder.layer.{i}.layernorm_before.weight""")						)
      rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""deit.encoder.layer.{i}.layernorm_before.bias""")						)
      rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""deit.encoder.layer.{i}.attention.output.dense.weight""")						)
      rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""deit.encoder.layer.{i}.attention.output.dense.bias""")						)
      rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""deit.encoder.layer.{i}.layernorm_after.weight""")						)
      rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""deit.encoder.layer.{i}.layernorm_after.bias""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""deit.encoder.layer.{i}.intermediate.dense.weight""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""deit.encoder.layer.{i}.intermediate.dense.bias""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""deit.encoder.layer.{i}.output.dense.weight""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""deit.encoder.layer.{i}.output.dense.bias""")						)

   # projection layer + position embeddings
   rename_keys.extend(
       [
           ('cls_token', 'deit.embeddings.cls_token'),
           ('dist_token', 'deit.embeddings.distillation_token'),
           ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
           ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
           ('pos_embed', 'deit.embeddings.position_embeddings'),
       ]						)

   if base_model:
      # layernorm + pooler
      rename_keys.extend(
          [
              ('norm.weight', 'layernorm.weight'),
              ('norm.bias', 'layernorm.bias'),
              ('pre_logits.fc.weight', 'pooler.dense.weight'),
              ('pre_logits.fc.bias', 'pooler.dense.bias'),
          ]						)

      # if just the base model, we should remove "deit" from all keys that start with "deit"
      A__       =     [(pair[0], pair[1][4:]) if pair[1].startswith('deit'						) else pair for pair in rename_keys]
   else:
      # layernorm + classification heads
      rename_keys.extend(
          [
              ('norm.weight', 'deit.layernorm.weight'),
              ('norm.bias', 'deit.layernorm.bias'),
              ('head.weight', 'cls_classifier.weight'),
              ('head.bias', 'cls_classifier.bias'),
              ('head_dist.weight', 'distillation_classifier.weight'),
              ('head_dist.bias', 'distillation_classifier.bias'),
          ]						)

   return rename_keys







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=False						):






   '''simple docstring'''







   for i in range(config.num_hidden_layers						):
      if base_model:
         A__       =     ''
      else:
         A__       =     'deit.'
      # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
      A__       =     state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""						)
      A__       =     state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""						)
      # next, add query, keys and values (in that order) to the state dict
      A__       =     in_proj_weight[
          : config.hidden_size, :
      ]
      A__       =     in_proj_bias[: config.hidden_size]
      A__       =     in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      A__       =     in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      A__       =     in_proj_weight[
          -config.hidden_size :, :
      ]
      A__       =     in_proj_bias[-config.hidden_size :]







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     dct.pop(UpperCAmelCase						)
   A__       =     val







def 						_A      ( ):






   '''simple docstring'''







   A__       =     'http://images.cocodataset.org/val2017/000000039769.jpg'
   A__       =     Image.open(requests.get(UpperCAmelCase      ,stream=UpperCAmelCase						).raw						)
   return im







@torch.no_grad()
def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     DeiTConfig()
   # all deit models have fine-tuned heads
   A__       =     False
   # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
   A__       =     1000
   A__       =     'huggingface/label-files'
   A__       =     'imagenet-1k-id2label.json'
   A__       =     json.load(open(hf_hub_download(UpperCAmelCase      ,UpperCAmelCase      ,repo_type='dataset'						)      ,'r'						)						)
   A__       =     {int(UpperCAmelCase						): v for k, v in idalabel.items()}
   A__       =     idalabel
   A__       =     {v: k for k, v in idalabel.items()}
   A__       =     int(deit_name[-6:-4]						)
   A__       =     int(deit_name[-3:]						)
   # size of the architecture
   if deit_name[9:].startswith('tiny'						):
      A__       =     192
      A__       =     768
      A__       =     12
      A__       =     3
   elif deit_name[9:].startswith('small'						):
      A__       =     384
      A__       =     1536
      A__       =     12
      A__       =     6
   if deit_name[9:].startswith('base'						):
      pass
   elif deit_name[4:].startswith('large'						):
      A__       =     1024
      A__       =     4096
      A__       =     24
      A__       =     16

   # load original model from timm
   A__       =     timm.create_model(UpperCAmelCase      ,pretrained=UpperCAmelCase						)
   timm_model.eval()

   # load state_dict of original model, remove and rename some keys
   A__       =     timm_model.state_dict()
   A__       =     create_rename_keys(UpperCAmelCase      ,UpperCAmelCase						)
   for src, dest in rename_keys:
      rename_key(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)
   read_in_q_k_v(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)

   # load HuggingFace model
   A__       =     DeiTForImageClassificationWithTeacher(UpperCAmelCase						).eval()
   model.load_state_dict(UpperCAmelCase						)

   # Check outputs on an image, prepared by DeiTImageProcessor
   A__       =     int(
       (256 / 224) * config.image_size						)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
   A__       =     DeiTImageProcessor(size=UpperCAmelCase      ,crop_size=config.image_size						)
   A__       =     image_processor(images=prepare_img()      ,return_tensors='pt'						)
   A__       =     encoding['pixel_values']
   A__       =     model(UpperCAmelCase						)

   A__       =     timm_model(UpperCAmelCase						)
   assert timm_logits.shape == outputs.logits.shape
   assert torch.allclose(UpperCAmelCase      ,outputs.logits      ,atol=1e-3						)

   Path(UpperCAmelCase						).mkdir(exist_ok=UpperCAmelCase						)
   print(F"""Saving model {deit_name} to {pytorch_dump_folder_path}"""						)
   model.save_pretrained(UpperCAmelCase						)
   print(F"""Saving image processor to {pytorch_dump_folder_path}"""						)
   image_processor.save_pretrained(UpperCAmelCase						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--deit_name''',
        default='''vit_deit_base_distilled_patch16_224''',
        type=str,
        help='''Name of the DeiT timm model you\'d like to convert.''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
    )

    lowerCAmelCase_     						=				parser.parse_args()
    convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)


